Earticle

초록

영어

One of the most important tools in security field is Intrusion Detection System. The aim of the IDS is to monitor suspicious network traffic and generate alerts. These systems are known to generate numerousfalse positive alerts. Analyzing the alerts manually by security expert need more time and could be error prone.Another problem with IDS is Identifying attack types and generating correct alerts related to attacks.we introducenew alert management systems to overcome mentioned problems. Alert management systems help security experts to manage alerts and produce a high level view of alerts. In this paper a new alert clustering algorithm for IDS Alert Management System proposed that uses the K-mean Based Genetic (KBG). The proposed algorithm reduces alerts and detects false positive alerts. By the experimental results on DARPA KDD cup 98 the system is able to cluster and classify alerts and causes reducing false positive alerts considerably.

목차

Abstract
 1. Introduction
 2. Related Works
 3. Proposed Alert Management System Based on Kbg
  3.1. Labeling Unit
  3.2. Normalization and Filtering Unit
  3.3. Preprocessing Unit
  3.4. K-means Based Genetic Algorithm (Cluster/Classify) Unit
 4. KBG Clustering
 5. Experimental Results
 6. Future Works
 References

키워드

저자

• Mohammad Masdari [ Department of Computer Engineering, Islamic Azad University, Science and Research Branch, Urmia ]
• Fatemeh Charlank Bakhtiari [ Department of Computer Engineering, Islamic Azad University, Science and Research Branch, Urmia ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Security and Its Applications

• 간기

  격월간

• pISSN

  1738-9976

• 수록기간

  2008~2016

• 등재여부

  SCOPUS

• 십진분류

  KDC 505 DDC 605